System and method for location based error correcting and video transcribing code selection

Abstract

A computer-implemented method includes receiving a plurality of data associated with a device, wherein the plurality of data includes a location data associated with the device; applying the plurality of data as an input to a trained Machine Learning (ML) model to determine an error correcting code to be used for the device based on an output of the selected ML model; and sending the error correcting code to the device.

What is claimed is: 
     
       1. A computer-implemented method, comprising:
 receiving a plurality of data associated with a device, wherein the plurality of data includes a location data associated with the device; 
 applying the plurality of data as an input to a trained Machine Learning (ML) model to determine an error correcting code to be used for the device based on an output of the ML model; 
 sending the error correcting code to the device; 
 determining a transcoding to be used for the device as a parameter of the determined error correcting code; and 
 transmitting a signal to a source device to change an already in use transcoding to the determined transcoding. 
 
     
     
       2. The computer-implemented method of  claim 1 , wherein the error correcting code is forward error correction (FEC). 
     
     
       3. The computer-implemented method of  claim 1 , further comprising determining a percentage of redundancy as a parameter of the determined error correcting code. 
     
     
       4. The computer-implemented of  claim 1 , further comprising receiving environmental data that includes one or more of density of users within a given geographical location, load or congestion of a network, connectivity type, network type, weather, signal fade associated with the location of the device, and proximity/direction to signal source, and wherein the applying and the determining is further based on the environment data. 
     
     
       5. The computer-implemented of  claim 1 , wherein the plurality of data includes one or more of device type data, time data associated with the device, speed of travel associated with the device, acceleration associated with the device, and error correcting code being used by the device. 
     
     
       6. The computer-implemented method of  claim 1 , further comprising:
 transmitting a signal to the device and to a source device, wherein the signal causes the device and the source device to utilize the determined error correcting code during communication; and 
 causing the device and the source device to change a percentage of redundancy to be used in association with the determined error correcting code. 
 
     
     
       7. The computer-implemented method of  claim 1 , further comprising selecting a machine learning model (ML) from a plurality of ML models based on the location data. 
     
     
       8. A non-transitory, computer-readable medium storing a set of instructions that, when executed by a processor, cause:
 receiving a plurality of data associated with a device, wherein the plurality of data includes a location data associated with the device; 
 applying the plurality of data as an input to a trained Machine Learning (ML) model to determine an error correcting code to be used for the device based on an output of the ML model; 
 sending the error correcting code to the device; 
 determining a transcoding to be used for the device as a parameter of the determined error correcting code; and 
 transmitting a signal to a source device to change an already in use transcoding to the determined transcoding. 
 
     
     
       9. The non-transitory, computer-readable medium of  claim 8 , wherein the error correcting code is forward error correction (FEC). 
     
     
       10. The non-transitory, computer-readable medium of  claim 8 , wherein when executed by a processor, further causes determining a percentage of redundancy as a parameter of the determined error correcting code. 
     
     
       11. The non-transitory, computer-readable medium of  claim 8 , wherein when executed by a processor, further causes receiving environmental data that includes one or more of density of users within a given geographical location, load or congestion of a network, connectivity type, network type, weather, signal fade associated with the location of the device, and proximity/direction to signal source, and wherein the applying and the determining is further based on the environment data. 
     
     
       12. The non-transitory, computer-readable medium of  claim 8 , wherein the plurality of data includes one or more of device type data, time data associated with the device, speed of travel associated with the device, acceleration associated with the device, and error correcting code being used by the device. 
     
     
       13. A system comprising:
 a memory storing a set of instructions; and 
 at least one processor configured to execute the instructions to:
 receive a plurality of data associated with a device, wherein the plurality of data includes a location data associated with the device; 
 apply the plurality of data as an input to a trained Machine Learning (ML) model to determine an error correcting code to be used for the device based on an output of the ML model; 
 
 send the error correcting code to the device; 
 determine a transcoding to be used for the device as a parameter of the determined error correcting code; and 
 transmit a signal to a source device to change an already in use transcoding to the determined transcoding. 
 
     
     
       14. The system of  claim 13 , wherein the error correcting code is forward error correction (FEC). 
     
     
       15. The system of  claim 13 , wherein the at least one processor is configured to execute the instructions to determine a percentage of redundancy as a parameter of the determined error correcting code. 
     
     
       16. The system of  claim 13 , wherein the at least one processor is configured to execute the instructions to receive environmental data that includes one or more of density of users within a given geographical location, load or congestion of a network, connectivity type, network type, weather, signal fade associated with the location of the device, and proximity/direction to signal source, and wherein the applying and the determining is further based on the environment data. 
     
     
       17. The system of  claim 13 , wherein the plurality of data includes one or more of device type data, time data associated with the device, speed of travel associated with the device, acceleration associated with the device, and error correcting code being used by the device.